Enhancing Radar Image Classification with Autoencoder-CNN Hybrid System

Kürşad Uçar

Makale detayları

Enhancing Radar Image Classification with Autoencoder-CNN Hybrid System

Yazarlar:

Kürşad Uçar

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Artificial Intelligence (Other) | Radio Frequency Engineering

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.13 Issue 3, pp.578-586

Belge Tipi:

Makale

Yayın Tarihi:

9/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001327644

pISSN:

2147-3129

Anahtar kelimeler:

Özet:

 The tracking, analysis, and classification of human movements can be crucial, particularly in areas such as elderly care, healthcare, and infant care. Typically, such tracking is done remotely with cameras. However, radar systems have emerged as significant methods and tools for these tasks due to their advantages such as privacy, wireless operation, and the ability to work through walls. By converting reflected radar signals from targets into images, human activities can be classified using powerful classification tools like deep learning. In this study, range-Doppler images of behind-the-wall human movements obtained with a radar system consisting of one transmitter and four receiver antennas were classified. Since the data collected from the four receiver antennas are in different positions, the collected reflection signals also differ. The signals collected with the range-time matrix content were divided into positive and negative parts, resulting in eight images from the four antennas. Instead of using all the data in CNN training, the images were first subjected to a reconstruction process with an autoencoder to reduce differences. As a result, it was observed that reconstructing the images with an autoencoder before classification with CNN increased the classification success. In conclusion, it was observed that the classification success of radar images can be increased by using a hybrid system with an autoencoder to reconstruct the images before classification with CNN.

 The tracking, analysis, and classification of human movements can be crucial, particularly in areas such as elderly care, healthcare, and infant care. Typically, such tracking is done remotely with cameras. However, radar systems have emerged as significant methods and tools for these tasks due to their advantages such as privacy, wireless operation, and the ability to work through walls. By converting reflected radar signals from targets into images, human activities can be classified using powerful classification tools like deep learning. In this study, range-Doppler images of behind-the-wall human movements obtained with a radar system consisting of one transmitter and four receiver antennas were classified. Since the data collected from the four receiver antennas are in different positions, the collected reflection signals also differ. The signals collected with the range-time matrix content were divided into positive and negative parts, resulting in eight images from the four antennas. Instead of using all the data in CNN training, the images were first subjected to a reconstruction process with an autoencoder to reduce differences. As a result, it was observed that reconstructing the images with an autoencoder before classification with CNN increased the classification success. In conclusion, it was observed that the classification success of radar images can be increased by using a hybrid system with an autoencoder to reconstruct the images before classification with CNN.

 The tracking, analysis, and classification of human movements can be crucial, particularly in areas such as elderly care, healthcare, and infant care. Typically, such tracking is done remotely with cameras. However, radar systems have emerged as significant methods and tools for these tasks due to their advantages such as privacy, wireless operation, and the ability to work through walls. By converting reflected radar signals from targets into images, human activities can be classified using powerful classification tools like deep learning. In this study, range-Doppler images of behind-the-wall human movements obtained with a radar system consisting of one transmitter and four receiver antennas were classified. Since the data collected from the four receiver antennas are in different positions, the collected reflection signals also differ. The signals collected with the range-time matrix content were divided into positive and negative parts, resulting in eight images from the four antennas. Instead of using all the data in CNN training, the images were first subjected to a reconstruction process with an autoencoder to reduce differences. As a result, it was observed that reconstructing the images with an autoencoder before classification with CNN increased the classification success. In conclusion, it was observed that the classification success of radar images can be increased by using a hybrid system with an autoencoder to reconstruct the images before classification with CNN.

 The tracking, analysis, and classification of human movements can be crucial, particularly in areas such as elderly care, healthcare, and infant care. Typically, such tracking is done remotely with cameras. However, radar systems have emerged as significant methods and tools for these tasks due to their advantages such as privacy, wireless operation, and the ability to work through walls. By converting reflected radar signals from targets into images, human activities can be classified using powerful classification tools like deep learning. In this study, range-Doppler images of behind-the-wall human movements obtained with a radar system consisting of one transmitter and four receiver antennas were classified. Since the data collected from the four receiver antennas are in different positions, the collected reflection signals also differ. The signals collected with the range-time matrix content were divided into positive and negative parts, resulting in eight images from the four antennas. Instead of using all the data in CNN training, the images were first subjected to a reconstruction process with an autoencoder to reduce differences. As a result, it was observed that reconstructing the images with an autoencoder before classification with CNN increased the classification success. In conclusion, it was observed that the classification success of radar images can be increased by using a hybrid system with an autoencoder to reconstruct the images before classification with CNN.

Makale detayları

Enhancing Radar Image Classification with Autoencoder-CNN Hybrid System

Yazarlar:

Kürşad Uçar

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Artificial Intelligence (Other) | Radio Frequency Engineering

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.13 Issue 3, pp.578-586

Belge Tipi:

Makale

Yayın Tarihi:

9/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001327644

pISSN:

2147-3129

Autoencoder Reconstruction

Behind-the-Wall Monitoring

CNN Classification

Human Movement Tracking

Radar Systems

Sayı

Makale detayları

Enhancing Radar Image Classification with Autoencoder-CNN Hybrid System

Yazarlar: Kürşad Uçar

Yayıncı: Bitlis Eren University

Ana Başlıklar: Artificial Intelligence (Other) | Radio Frequency Engineering

Dergi: Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.13 Issue 3, pp.578-586

Belge Tipi: Makale

Yayın Tarihi: 9/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001327644

pISSN: 2147-3129

Autoencoder Reconstruction

Behind-the-Wall Monitoring

CNN Classification

Human Movement Tracking

Radar Systems

Sayı

Yazarlar:

Kürşad Uçar

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Artificial Intelligence (Other) | Radio Frequency Engineering

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.13 Issue 3, pp.578-586

Belge Tipi:

Makale

Yayın Tarihi:

9/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001327644

pISSN:

2147-3129